DRIVER DROWSINESS DETECTION AND ALARMING SYSTEM
Mr E. Sankar, Bommisetti Hemanth Gopal Sai Krishna, Bommisettis Praneeth
Assistant Professor, Dept. Of CSE, SCSVMV (Deemed to be University)
Student, Dept. Of CSE, SCSVMV (Deemed to be University)
Student, Dept. Of CSE, SCSVMV (Deemed to be University)
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Abstract
Lack of sleep is a significant contributing factor in auto accidents. Due to the greater speeds and slower reaction times, there is a substantial likelihood that drowsy driving may result in accidents. This project's goal is to advance a system. to identify driving sleepiness signs. The strategy is to examine the driver's live video clip frame by frame, looking for the driver's eyes and then examining whether they are open or closed.The system issues an alarm alert, sends a text message of warning, and sends the driver's GPS location to a list of contacts if the driver's eyes are closed for more than five seconds
The main goal of this research is to create a sleepiness detection system that monitors the eyes; it is thought that by identifying the signs of driver fatigue early, an accident can be prevented. When this happens when drowsiness is found, a warning signal is sent to the driver to let them know. This detection device enables early identification of a reduction in driver alertness while driving and offers a noncontact technique for evaluating various levels of driver attentiveness. When this happens when weariness is found, a warning signal is sent to the driver to let them know. If the driver doesn't react to the alarm, the system also includes a mechanism that will slow down the car until it stops.
Among the major contributing factors to traffic accidents are driver fatigue and drowsiness. The numbers of fatalities and injuries worldwide are rising each year. In this paper, a module for the Advanced Driver Assistance System (ADAS) is presented. This system deals with automatic driver drowsiness detection based on visual data and Artificial Intelligence, thereby reducing the number of accidents caused by drivers fatigue and increasing transportation safety. In order to measure PERCLOS, a measure of tiredness linked with sluggish eye closure that has received scientific backing, we present an algorithm to find, track, and analyse both the driver's face and eyes.
Key Words: Dlib, Eye Aspect Ratio, Face detection, facial landmarks, OpenCV..
